Data-Driven Decisions 1
Let's take a moment to look at grammar .... strictly-speaking, data is a plural term. Ie, if we're following the rules of grammar, we shouldn't write "the data is" or "the data shows" but instead "the data are" or "the data show". That being said, no one talks like that, so suck it.
DO NOW - BREAD DATA
How do you use data in your district? AP Students at CHS (Slides)
Topic 1: FRAMEWORK OF DATA-DRIVEN DECISION MAKING
The process of continuous school improvement may be accomplished through data-driven decision making (DDDM). DDDM is the collection, analysis, and reporting of student assessment data and relevant background information used to guide decisions related to the planning and implementing of instructional strategies in the classroom for individual students toward school improvement (AASA, 2002; Cradler, 2010). The progression towards sustaining continuous improvement utilizing DDDM is cyclic in nature (Boudett, City, & Murnane, 2006). It is imperative schools advance through and repeat the following four-step approach of DDDM in order to perpetuate school improvement.
Step One: Establish a Data Friendly Environment while Reviewing the School’s Improvement Plan
Administrators and staff should collaboratively review the improvement plan to determine the need for and use of data collection (Williamson & Blackburn, 2009). Once the areas in need of improvement have been established, goals must be set to clearly define the school’s direction (Flowers & Carpenter, 2009). As the collective group prioritizes the goals and works through this process, an environment establishing the importance of compiling and using data is created (Ellingsen, 2007). In order to further build a level of comfort during data collection, an administrator will want to help staff become literate in reading assessment reports (Boudett, City, & Murnane, 2006). Therefore, sessions to assist staff in becoming familiar with the various types of data, certain language utilized in data reports, and key concepts are essential to a better understanding.
Step Two: Data Collection
The usage of multiple data sources influences the quality of the data collected (Texas Education Agency, 2009). Therefore, a list of data sources that could be used to make informed decisions about the focus area should be generated. Once the data presently housed on the campus are located, the group will need to determine how the data can be used to meet the goals. For example, this could include walk-through reports, curriculum guides, lesson plans, and standardized test scores (Williamson & Blackburn, 2009). Additional data pertaining to the focus area may also be collected from teacher, parent, and student surveys.
Step Three: Examine and Discuss the Data
Administrators should engage staff in meaningful dialogue concerning what they see in the data. In-depth conversations surrounding the data can alleviate individual pre-determined notions about what the data show and will keep everyone focused on examining the results. Questions such as What do the data tell them? Is their school where they want it to be? What do the data not tell them? and What additional information is needed? should be considered during these candid conversations (Flowers & Carpenter, 2009). After analyzing data from each data source, patterns across sources may be observed (Williamson & Blackburn, 2009), after which a shared examination of campus instruction should ensue. A discussion of effective instructional practices along with assistance to staff in the examination of what is happening on the campus can be compared to the kind of effective instruction needed for continuous improvement (Boudett, City, & Murnane, 2009).
We also need to BREAK DOWN the data to reveal results withing specific groups in the school community -- grade-level, race, ethnic groups, special ed, ELL, gender, etc
Step Four: Construct the Plan and Evaluate Progress
Develop short-term and long-term goals indicating where the campus would like to be in the area of concern within the next 1-5 years. When creating the action plan, administrators and staff should ascertain what resources are available to help reach the goals, select strategies that will lead to achieving the campus needs, and identify professional development activities necessary to provide support in the implementation of the strategies (Boudett, City, & Murnane, 2006; Flowers & Carpenter, 2009). Finally, consider how progress will be measured. Formative and summative evaluations should occur throughout the year to assess any successes or deficiencies of the plan that need to be addressed.
Topic 2: Using multiple sources of data as diagnostic tools
Part 1 Data Analysis Task & Activity
Principals are required to interpret and use data to inform school management and instructional support. The Data Analysis Task is intended to provide insights into areas of candidate strength and weakness with respect to analyzing and using data to inform instructional leadership decisions. During this activity, a candidate is asked to review three scenarios and react to several short-answer questions. The scenarios in this activity have been adapted from a set of scenarios used by Means, Padilla, DeBarger, and Bakia (2009), who created a validated instrument for assessing teacher capabilities to make data-based instructional decisions. Activity Answers
Part 2 Who should be part of the data-gathering process?
Administrators using Evaluation Tools (NJ.COM) Look at my district's 2018 DATA CARD.
Students? See the Gates 7Cs Survey Recent article about this from Australia.
Teachers? Check out the ASCD article "A Day's Worth of Data," "Improving Teaching and Learning with Data-Based Decisions: Asking the Right Questions and Acting on the Answers" article, and the Action Research example below. MILLBURN PEER VISIT
Parents? Check out school surveys below
ACTIVITY 2: Managing PLC Data Plan & AchieveNJ's mSGP Analysis
Collect as much of the following data from your school/district as possible for review, analysis and discussion in class: (a) past 3 years of state assessment results (by class and grade level benchmarks); (b) past 3 years of other standardized test results (by class and grade level benchmarks; include item and/or cluster analysis); (c) results of any grade level district-created/school-created/teacher-created assessments (i.e. writing samples, mid-term/final exams) for the same benchmark grade levels; (d) results of any school culture or climate surveys; (e) results of any parent surveys; (f) student demographic information for the past 3 years (e.g. enrollment by grade, average class size, attendance rates, gender, race/ethnicity, number of ELL/English Language Learners and special education students, mobility rates, discipline/suspension rates; drop-out/graduation rates); (g) relevant teacher demographics (i.e. number emergency or teaching out of certification area, average years of teaching experience, attrition/turnover rates); and (h) any other relevant data related to factors that indirectly and directly affect student learning. Be prepared to discuss your analysis in class.
TOPIC 3: The Data Carousel
To begin class, we will watch this 7-minute, Teaching Channel video to answer the discussion questions. Also, there's a great EduTopia video caled "Making Sure Every Child is Seen."
Today's Resources
1. Bill Gates7Cs Teacher Surveys
https://docs.gatesfoundation.org/documents/preliminary-finding-policy-brief.pdf
2. What does it mean to be seen?
https://www.edutopia.org/article/power-being-seen
3. NJ DOE School Performance Report
http://education.state.nj.us/pr/
4. Frank's Action Research Draft
Data-Driven Decisions 1 Data-Driven Decisions 2 Data-Driven Decisions 3 Data-Driven Decisions 4
Data Module Assessment Practice
Click on the link above to apply your module knowledge with a SLLA Sample Test
& remember to complete the NJEXCEL Module Instructor Evaluation too
Recent Articles (Updated Monthly)
Although your module might be over, come back to this
site for recent articles and/or books on data use in schools